Growing gene network by integration of gene expression, motif sequence and metabolic information

Bo Geng; Xiaobo Zhou; Y. S. Hung; Stephen T. Wong

doi:10.1063/1.2816632

Growing gene network by integration of gene expression, motif sequence and metabolic information

Bo Geng, Xiaobo Zhou, Y. S. Hung, Stephen T. Wong

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In computational biology, gene networks are typically inferred from gene expression data alone. Incorporating multiple types of biological information makes it possible to improve gene network estimation. In this paper, we describe an approach for growing gene network from a sub-network by the integration of gene expression data, motif sequence, and metabolic information. To evaluate the approach, we apply it to a pool of E.coli genes related to aspartate pathway. The results show that integrative approach has potentials of reconstructing more accurate gene networks.

Original language	English (US)
Title of host publication	Computational Models For Life Sciences (CMLS '07) - 2007 International Symposium
Pages	279-286
Number of pages	8
Volume	952
DOIs	https://doi.org/10.1063/1.2816632
State	Published - Dec 1 2007
Event	2007 International Symposium on Computational Models for Life Sciences, CMLS '07 - Gold Coast, QLD, Australia Duration: Dec 17 2007 → Dec 19 2007

Other

Other	2007 International Symposium on Computational Models for Life Sciences, CMLS '07
Country/Territory	Australia
City	Gold Coast, QLD
Period	12/17/07 → 12/19/07

Keywords

Gene expression
Gene network growing
Metabolic reaction
Motif sequence

ASJC Scopus subject areas

Physics and Astronomy(all)

Access to Document

10.1063/1.2816632

Cite this

Geng, B, Zhou, X, Hung, YS & Wong, ST 2007, Growing gene network by integration of gene expression, motif sequence and metabolic information. in Computational Models For Life Sciences (CMLS '07) - 2007 International Symposium. vol. 952, pp. 279-286, 2007 International Symposium on Computational Models for Life Sciences, CMLS '07, Gold Coast, QLD, Australia, 12/17/07. https://doi.org/10.1063/1.2816632

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abstract = "In computational biology, gene networks are typically inferred from gene expression data alone. Incorporating multiple types of biological information makes it possible to improve gene network estimation. In this paper, we describe an approach for growing gene network from a sub-network by the integration of gene expression data, motif sequence, and metabolic information. To evaluate the approach, we apply it to a pool of E.coli genes related to aspartate pathway. The results show that integrative approach has potentials of reconstructing more accurate gene networks.",

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AU - Zhou, Xiaobo

AU - Hung, Y. S.

AU - Wong, Stephen T.

PY - 2007/12/1

Y1 - 2007/12/1

N2 - In computational biology, gene networks are typically inferred from gene expression data alone. Incorporating multiple types of biological information makes it possible to improve gene network estimation. In this paper, we describe an approach for growing gene network from a sub-network by the integration of gene expression data, motif sequence, and metabolic information. To evaluate the approach, we apply it to a pool of E.coli genes related to aspartate pathway. The results show that integrative approach has potentials of reconstructing more accurate gene networks.

AB - In computational biology, gene networks are typically inferred from gene expression data alone. Incorporating multiple types of biological information makes it possible to improve gene network estimation. In this paper, we describe an approach for growing gene network from a sub-network by the integration of gene expression data, motif sequence, and metabolic information. To evaluate the approach, we apply it to a pool of E.coli genes related to aspartate pathway. The results show that integrative approach has potentials of reconstructing more accurate gene networks.

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KW - Gene network growing

KW - Metabolic reaction

KW - Motif sequence

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DO - 10.1063/1.2816632

M3 - Conference contribution

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EP - 286

BT - Computational Models For Life Sciences (CMLS '07) - 2007 International Symposium

T2 - 2007 International Symposium on Computational Models for Life Sciences, CMLS '07

Y2 - 17 December 2007 through 19 December 2007

ER -

Growing gene network by integration of gene expression, motif sequence and metabolic information

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Keywords

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